Overview

Dataset statistics

Number of variables21
Number of observations1991
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory326.8 KiB
Average record size in memory168.1 B

Variable types

Numeric14
Categorical7

Alerts

fc is highly overall correlated with pcHigh correlation
four_g is highly overall correlated with three_gHigh correlation
pc is highly overall correlated with fcHigh correlation
price_range is highly overall correlated with ramHigh correlation
ram is highly overall correlated with price_rangeHigh correlation
three_g is highly overall correlated with four_gHigh correlation
price_range is uniformly distributedUniform
fc has 471 (23.7%) zerosZeros
pc has 100 (5.0%) zerosZeros
sc_w has 178 (8.9%) zerosZeros

Reproduction

Analysis started2024-05-03 23:37:56.941231
Analysis finished2024-05-03 23:38:25.977773
Duration29.04 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

battery_power
Real number (ℝ)

Distinct1090
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1238.2707
Minimum501
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2024-05-04T02:38:26.087708image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile570.5
Q1851.5
median1225
Q31615.5
95-th percentile1929.5
Maximum1998
Range1497
Interquartile range (IQR)764

Descriptive statistics

Standard deviation439.64106
Coefficient of variation (CV)0.35504438
Kurtosis-1.225982
Mean1238.2707
Median Absolute Deviation (MAD)382
Skewness0.031851485
Sum2465397
Variance193284.26
MonotonicityNot monotonic
2024-05-04T02:38:26.251013image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
618 6
 
0.3%
1872 6
 
0.3%
1589 6
 
0.3%
1949 5
 
0.3%
1083 5
 
0.3%
504 5
 
0.3%
772 5
 
0.3%
1310 5
 
0.3%
1413 5
 
0.3%
1330 5
 
0.3%
Other values (1080) 1938
97.3%
ValueCountFrequency (%)
501 2
 
0.1%
502 2
 
0.1%
503 3
0.2%
504 5
0.3%
506 1
 
0.1%
507 2
 
0.1%
508 3
0.2%
509 1
 
0.1%
510 3
0.2%
511 4
0.2%
ValueCountFrequency (%)
1998 1
 
0.1%
1997 1
 
0.1%
1996 2
0.1%
1995 1
 
0.1%
1994 3
0.2%
1993 1
 
0.1%
1992 2
0.1%
1991 4
0.2%
1989 2
0.1%
1988 1
 
0.1%

blue
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
0
1003 
1
988 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1991
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1003
50.4%
1 988
49.6%

Length

2024-05-04T02:38:26.396835image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:38:26.504318image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1003
50.4%
1 988
49.6%

Most occurring characters

ValueCountFrequency (%)
0 1003
50.4%
1 988
49.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1991
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1003
50.4%
1 988
49.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1991
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1003
50.4%
1 988
49.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1003
50.4%
1 988
49.6%

clock_speed
Real number (ℝ)

Distinct26
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.522451
Minimum0.5
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2024-05-04T02:38:26.624875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q10.7
median1.5
Q32.2
95-th percentile2.8
Maximum3
Range2.5
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.81549887
Coefficient of variation (CV)0.53564867
Kurtosis-1.3216686
Mean1.522451
Median Absolute Deviation (MAD)0.8
Skewness0.1776212
Sum3031.2
Variance0.66503841
MonotonicityNot monotonic
2024-05-04T02:38:26.749891image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.5 410
20.6%
2.8 84
 
4.2%
2.3 78
 
3.9%
2.1 76
 
3.8%
1.6 76
 
3.8%
2.5 74
 
3.7%
0.6 74
 
3.7%
1.4 70
 
3.5%
1.3 68
 
3.4%
1.5 67
 
3.4%
Other values (16) 914
45.9%
ValueCountFrequency (%)
0.5 410
20.6%
0.6 74
 
3.7%
0.7 64
 
3.2%
0.8 58
 
2.9%
0.9 57
 
2.9%
1 61
 
3.1%
1.1 50
 
2.5%
1.2 56
 
2.8%
1.3 68
 
3.4%
1.4 70
 
3.5%
ValueCountFrequency (%)
3 28
 
1.4%
2.9 62
3.1%
2.8 84
4.2%
2.7 54
2.7%
2.6 55
2.8%
2.5 74
3.7%
2.4 57
2.9%
2.3 78
3.9%
2.2 59
3.0%
2.1 76
3.8%

dual_sim
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
1
1017 
0
974 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1991
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 1017
51.1%
0 974
48.9%

Length

2024-05-04T02:38:26.879624image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:38:26.967848image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1017
51.1%
0 974
48.9%

Most occurring characters

ValueCountFrequency (%)
1 1017
51.1%
0 974
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1991
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1017
51.1%
0 974
48.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1991
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1017
51.1%
0 974
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1017
51.1%
0 974
48.9%

fc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3078855
Minimum0
Maximum19
Zeros471
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2024-05-04T02:38:27.080340image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile13
Maximum19
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.339735
Coefficient of variation (CV)1.0073933
Kurtosis0.27061826
Mean4.3078855
Median Absolute Deviation (MAD)3
Skewness1.0183373
Sum8577
Variance18.8333
MonotonicityNot monotonic
2024-05-04T02:38:27.188520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 471
23.7%
1 245
12.3%
2 189
9.5%
3 170
 
8.5%
5 137
 
6.9%
4 132
 
6.6%
6 110
 
5.5%
7 100
 
5.0%
9 78
 
3.9%
8 77
 
3.9%
Other values (10) 282
14.2%
ValueCountFrequency (%)
0 471
23.7%
1 245
12.3%
2 189
9.5%
3 170
 
8.5%
4 132
 
6.6%
5 137
 
6.9%
6 110
 
5.5%
7 100
 
5.0%
8 77
 
3.9%
9 78
 
3.9%
ValueCountFrequency (%)
19 1
 
0.1%
18 11
 
0.6%
17 6
 
0.3%
16 23
 
1.2%
15 23
 
1.2%
14 20
 
1.0%
13 40
2.0%
12 45
2.3%
11 51
2.6%
10 62
3.1%

four_g
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
1.0
1037 
0.0
954 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5973
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1037
52.1%
0.0 954
47.9%

Length

2024-05-04T02:38:27.352978image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:38:27.447808image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1037
52.1%
0.0 954
47.9%

Most occurring characters

ValueCountFrequency (%)
0 2945
49.3%
. 1991
33.3%
1 1037
 
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3982
66.7%
Other Punctuation 1991
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2945
74.0%
1 1037
 
26.0%
Other Punctuation
ValueCountFrequency (%)
. 1991
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2945
49.3%
. 1991
33.3%
1 1037
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2945
49.3%
. 1991
33.3%
1 1037
 
17.4%

int_memory
Real number (ℝ)

Distinct63
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.074335
Minimum2
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2024-05-04T02:38:27.572825image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q116
median32
Q348
95-th percentile61
Maximum64
Range62
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.138177
Coefficient of variation (CV)0.56550439
Kurtosis-1.2164324
Mean32.074335
Median Absolute Deviation (MAD)16
Skewness0.056349057
Sum63860
Variance328.99347
MonotonicityNot monotonic
2024-05-04T02:38:27.770454image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 47
 
2.4%
16 45
 
2.3%
14 45
 
2.3%
57 42
 
2.1%
2 41
 
2.1%
42 40
 
2.0%
7 40
 
2.0%
44 39
 
2.0%
30 39
 
2.0%
8 37
 
1.9%
Other values (53) 1576
79.2%
ValueCountFrequency (%)
2 41
2.1%
3 25
1.3%
4 20
1.0%
5 36
1.8%
6 36
1.8%
7 40
2.0%
8 37
1.9%
9 34
1.7%
10 36
1.8%
11 34
1.7%
ValueCountFrequency (%)
64 31
1.6%
63 30
1.5%
62 21
1.1%
61 26
1.3%
60 27
1.4%
59 18
0.9%
58 36
1.8%
57 42
2.1%
56 27
1.4%
55 29
1.5%

m_dep
Real number (ℝ)

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50200904
Minimum0.1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2024-05-04T02:38:27.891425image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.2
median0.5
Q30.8
95-th percentile1
Maximum1
Range0.9
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.28862172
Coefficient of variation (CV)0.5749333
Kurtosis-1.2748915
Mean0.50200904
Median Absolute Deviation (MAD)0.3
Skewness0.087386812
Sum999.5
Variance0.083302494
MonotonicityNot monotonic
2024-05-04T02:38:27.999449image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.1 320
16.1%
0.2 210
10.5%
0.8 206
10.3%
0.5 204
10.2%
0.7 200
10.0%
0.3 198
9.9%
0.9 195
9.8%
0.6 185
9.3%
0.4 167
8.4%
1 106
 
5.3%
ValueCountFrequency (%)
0.1 320
16.1%
0.2 210
10.5%
0.3 198
9.9%
0.4 167
8.4%
0.5 204
10.2%
0.6 185
9.3%
0.7 200
10.0%
0.8 206
10.3%
0.9 195
9.8%
1 106
 
5.3%
ValueCountFrequency (%)
1 106
 
5.3%
0.9 195
9.8%
0.8 206
10.3%
0.7 200
10.0%
0.6 185
9.3%
0.5 204
10.2%
0.4 167
8.4%
0.3 198
9.9%
0.2 210
10.5%
0.1 320
16.1%

mobile_wt
Real number (ℝ)

Distinct121
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.26318
Minimum80
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2024-05-04T02:38:28.156748image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile86
Q1109
median141
Q3170
95-th percentile196
Maximum200
Range120
Interquartile range (IQR)61

Descriptive statistics

Standard deviation35.398777
Coefficient of variation (CV)0.25237397
Kurtosis-1.2114924
Mean140.26318
Median Absolute Deviation (MAD)31
Skewness0.0039004393
Sum279264
Variance1253.0734
MonotonicityNot monotonic
2024-05-04T02:38:28.312993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182 28
 
1.4%
101 27
 
1.4%
185 27
 
1.4%
199 26
 
1.3%
146 26
 
1.3%
105 25
 
1.3%
198 24
 
1.2%
88 24
 
1.2%
89 24
 
1.2%
160 23
 
1.2%
Other values (111) 1737
87.2%
ValueCountFrequency (%)
80 21
1.1%
81 13
0.7%
82 15
0.8%
83 19
1.0%
84 17
0.9%
85 13
0.7%
86 19
1.0%
87 15
0.8%
88 24
1.2%
89 24
1.2%
ValueCountFrequency (%)
200 19
1.0%
199 26
1.3%
198 24
1.2%
197 19
1.0%
196 20
1.0%
195 11
0.6%
194 15
0.8%
193 15
0.8%
192 15
0.8%
191 15
0.8%

n_cores
Real number (ℝ)

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5163235
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2024-05-04T02:38:28.437234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2912571
Coefficient of variation (CV)0.50732794
Kurtosis-1.2333495
Mean4.5163235
Median Absolute Deviation (MAD)2
Skewness0.0079249959
Sum8992
Variance5.249859
MonotonicityNot monotonic
2024-05-04T02:38:28.561991image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 272
13.7%
7 258
13.0%
8 256
12.9%
2 247
12.4%
3 246
12.4%
5 244
12.3%
1 242
12.2%
6 226
11.4%
ValueCountFrequency (%)
1 242
12.2%
2 247
12.4%
3 246
12.4%
4 272
13.7%
5 244
12.3%
6 226
11.4%
7 258
13.0%
8 256
12.9%
ValueCountFrequency (%)
8 256
12.9%
7 258
13.0%
6 226
11.4%
5 244
12.3%
4 272
13.7%
3 246
12.4%
2 247
12.4%
1 242
12.2%

pc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9090909
Minimum0
Maximum20
Zeros100
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2024-05-04T02:38:28.725760image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q15
median10
Q315
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0610191
Coefficient of variation (CV)0.61166248
Kurtosis-1.1700299
Mean9.9090909
Median Absolute Deviation (MAD)5
Skewness0.021023941
Sum19729
Variance36.735952
MonotonicityNot monotonic
2024-05-04T02:38:28.876426image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
10 122
 
6.1%
7 119
 
6.0%
9 111
 
5.6%
20 110
 
5.5%
1 103
 
5.2%
14 103
 
5.2%
0 100
 
5.0%
2 99
 
5.0%
17 99
 
5.0%
6 95
 
4.8%
Other values (11) 930
46.7%
ValueCountFrequency (%)
0 100
5.0%
1 103
5.2%
2 99
5.0%
3 93
4.7%
4 95
4.8%
5 59
3.0%
6 95
4.8%
7 119
6.0%
8 89
4.5%
9 111
5.6%
ValueCountFrequency (%)
20 110
5.5%
19 83
4.2%
18 79
4.0%
17 99
5.0%
16 88
4.4%
15 92
4.6%
14 103
5.2%
13 83
4.2%
12 90
4.5%
11 79
4.0%

px_height
Real number (ℝ)

Distinct1132
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean644.8006
Minimum0
Maximum1960
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2024-05-04T02:38:29.047225image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile72
Q1282.5
median564
Q3947.5
95-th percentile1482
Maximum1960
Range1960
Interquartile range (IQR)665

Descriptive statistics

Standard deviation442.95103
Coefficient of variation (CV)0.68695816
Kurtosis-0.32238782
Mean644.8006
Median Absolute Deviation (MAD)318
Skewness0.66298592
Sum1283798
Variance196205.62
MonotonicityNot monotonic
2024-05-04T02:38:29.219664image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
347 7
 
0.4%
179 6
 
0.3%
275 6
 
0.3%
371 6
 
0.3%
327 5
 
0.3%
356 5
 
0.3%
211 5
 
0.3%
42 5
 
0.3%
667 5
 
0.3%
322 5
 
0.3%
Other values (1122) 1936
97.2%
ValueCountFrequency (%)
0 2
0.1%
1 1
 
0.1%
2 1
 
0.1%
3 2
0.1%
4 3
0.2%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
8 2
0.1%
9 1
 
0.1%
ValueCountFrequency (%)
1960 1
0.1%
1949 1
0.1%
1920 1
0.1%
1914 1
0.1%
1901 1
0.1%
1899 1
0.1%
1895 1
0.1%
1878 1
0.1%
1874 1
0.1%
1869 1
0.1%

px_width
Real number (ℝ)

Distinct1107
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1251.1155
Minimum500
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2024-05-04T02:38:29.385346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile578.5
Q1875.5
median1247
Q31633
95-th percentile1928.5
Maximum1998
Range1498
Interquartile range (IQR)757.5

Descriptive statistics

Standard deviation431.92489
Coefficient of variation (CV)0.34523182
Kurtosis-1.1854318
Mean1251.1155
Median Absolute Deviation (MAD)376
Skewness0.015751807
Sum2490971
Variance186559.11
MonotonicityNot monotonic
2024-05-04T02:38:29.551317image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
874 7
 
0.4%
1247 7
 
0.4%
1463 6
 
0.3%
1383 6
 
0.3%
1469 6
 
0.3%
1564 5
 
0.3%
1234 5
 
0.3%
1393 5
 
0.3%
1726 5
 
0.3%
1552 5
 
0.3%
Other values (1097) 1934
97.1%
ValueCountFrequency (%)
500 2
0.1%
501 2
0.1%
503 1
 
0.1%
506 1
 
0.1%
507 4
0.2%
508 1
 
0.1%
509 2
0.1%
510 3
0.2%
511 2
0.1%
512 2
0.1%
ValueCountFrequency (%)
1998 1
 
0.1%
1997 1
 
0.1%
1996 1
 
0.1%
1995 3
0.2%
1994 2
 
0.1%
1992 1
 
0.1%
1991 1
 
0.1%
1990 1
 
0.1%
1989 3
0.2%
1988 5
0.3%

ram
Real number (ℝ)

HIGH CORRELATION 

Distinct1556
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2123.6328
Minimum256
Maximum3998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2024-05-04T02:38:29.696368image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum256
5-th percentile445
Q11207
median2147
Q33065
95-th percentile3829.5
Maximum3998
Range3742
Interquartile range (IQR)1858

Descriptive statistics

Standard deviation1085.0513
Coefficient of variation (CV)0.5109411
Kurtosis-1.1935403
Mean2123.6328
Median Absolute Deviation (MAD)933
Skewness0.006836088
Sum4228153
Variance1177336.3
MonotonicityNot monotonic
2024-05-04T02:38:29.886058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1229 4
 
0.2%
1464 4
 
0.2%
2610 4
 
0.2%
2227 4
 
0.2%
3142 4
 
0.2%
595 3
 
0.2%
1300 3
 
0.2%
606 3
 
0.2%
1412 3
 
0.2%
3869 3
 
0.2%
Other values (1546) 1956
98.2%
ValueCountFrequency (%)
256 1
0.1%
258 2
0.1%
259 1
0.1%
262 1
0.1%
263 1
0.1%
265 1
0.1%
267 1
0.1%
273 1
0.1%
277 1
0.1%
278 2
0.1%
ValueCountFrequency (%)
3998 1
0.1%
3996 1
0.1%
3993 1
0.1%
3991 2
0.1%
3990 1
0.1%
3984 1
0.1%
3978 1
0.1%
3971 1
0.1%
3970 2
0.1%
3969 1
0.1%

sc_h
Real number (ℝ)

Distinct15
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.307885
Minimum5
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2024-05-04T02:38:30.036032image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median12
Q316
95-th percentile19
Maximum19
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.2130609
Coefficient of variation (CV)0.34230583
Kurtosis-1.1907561
Mean12.307885
Median Absolute Deviation (MAD)4
Skewness-0.098481222
Sum24505
Variance17.749883
MonotonicityNot monotonic
2024-05-04T02:38:30.165419image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
17 192
 
9.6%
12 157
 
7.9%
7 150
 
7.5%
16 143
 
7.2%
14 142
 
7.1%
15 134
 
6.7%
13 130
 
6.5%
11 126
 
6.3%
19 124
 
6.2%
10 124
 
6.2%
Other values (5) 569
28.6%
ValueCountFrequency (%)
5 96
4.8%
6 114
5.7%
7 150
7.5%
8 117
5.9%
9 123
6.2%
10 124
6.2%
11 126
6.3%
12 157
7.9%
13 130
6.5%
14 142
7.1%
ValueCountFrequency (%)
19 124
6.2%
18 119
6.0%
17 192
9.6%
16 143
7.2%
15 134
6.7%
14 142
7.1%
13 130
6.5%
12 157
7.9%
11 126
6.3%
10 124
6.2%

sc_w
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7744852
Minimum0
Maximum18
Zeros178
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2024-05-04T02:38:30.315855image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile14
Maximum18
Range18
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.3573852
Coefficient of variation (CV)0.75459285
Kurtosis-0.39109083
Mean5.7744852
Median Absolute Deviation (MAD)3
Skewness0.63323617
Sum11497
Variance18.986806
MonotonicityNot monotonic
2024-05-04T02:38:30.473021image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 208
10.4%
3 199
10.0%
4 182
9.1%
0 178
8.9%
5 159
 
8.0%
2 156
 
7.8%
7 131
 
6.6%
6 129
 
6.5%
8 125
 
6.3%
10 107
 
5.4%
Other values (9) 417
20.9%
ValueCountFrequency (%)
0 178
8.9%
1 208
10.4%
2 156
7.8%
3 199
10.0%
4 182
9.1%
5 159
8.0%
6 129
6.5%
7 131
6.6%
8 125
6.3%
9 97
4.9%
ValueCountFrequency (%)
18 8
 
0.4%
17 19
 
1.0%
16 29
 
1.5%
15 31
 
1.6%
14 33
 
1.7%
13 49
2.5%
12 67
3.4%
11 84
4.2%
10 107
5.4%
9 97
4.9%

talk_time
Real number (ℝ)

Distinct19
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.001507
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2024-05-04T02:38:30.611969image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median11
Q316
95-th percentile20
Maximum20
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.4648716
Coefficient of variation (CV)0.49673847
Kurtosis-1.2197223
Mean11.001507
Median Absolute Deviation (MAD)5
Skewness0.013605372
Sum21904
Variance29.864822
MonotonicityNot monotonic
2024-05-04T02:38:30.751375image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7 124
 
6.2%
4 123
 
6.2%
15 115
 
5.8%
16 114
 
5.7%
19 112
 
5.6%
6 111
 
5.6%
10 105
 
5.3%
8 104
 
5.2%
20 102
 
5.1%
11 101
 
5.1%
Other values (9) 880
44.2%
ValueCountFrequency (%)
2 98
4.9%
3 94
4.7%
4 123
6.2%
5 93
4.7%
6 111
5.6%
7 124
6.2%
8 104
5.2%
9 100
5.0%
10 105
5.3%
11 101
5.1%
ValueCountFrequency (%)
20 102
5.1%
19 112
5.6%
18 99
5.0%
17 98
4.9%
16 114
5.7%
15 115
5.8%
14 101
5.1%
13 99
5.0%
12 98
4.9%
11 101
5.1%

three_g
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
1
1515 
0
476 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1991
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1515
76.1%
0 476
 
23.9%

Length

2024-05-04T02:38:30.955191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:38:31.083583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1515
76.1%
0 476
 
23.9%

Most occurring characters

ValueCountFrequency (%)
1 1515
76.1%
0 476
 
23.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1991
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1515
76.1%
0 476
 
23.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1991
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1515
76.1%
0 476
 
23.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1515
76.1%
0 476
 
23.9%

touch_screen
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
1
999 
0
992 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1991
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 999
50.2%
0 992
49.8%

Length

2024-05-04T02:38:31.235435image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:38:31.420438image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 999
50.2%
0 992
49.8%

Most occurring characters

ValueCountFrequency (%)
1 999
50.2%
0 992
49.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1991
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 999
50.2%
0 992
49.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1991
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 999
50.2%
0 992
49.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 999
50.2%
0 992
49.8%

wifi
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
1
1006 
0
985 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1991
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 1006
50.5%
0 985
49.5%

Length

2024-05-04T02:38:31.610446image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:38:31.771456image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1006
50.5%
0 985
49.5%

Most occurring characters

ValueCountFrequency (%)
1 1006
50.5%
0 985
49.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1991
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1006
50.5%
0 985
49.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1991
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1006
50.5%
0 985
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1006
50.5%
0 985
49.5%

price_range
Categorical

HIGH CORRELATION  UNIFORM 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
2
499 
0
499 
3
497 
1
496 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1991
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 499
25.1%
0 499
25.1%
3 497
25.0%
1 496
24.9%

Length

2024-05-04T02:38:31.985730image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:38:32.153114image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2 499
25.1%
0 499
25.1%
3 497
25.0%
1 496
24.9%

Most occurring characters

ValueCountFrequency (%)
2 499
25.1%
0 499
25.1%
3 497
25.0%
1 496
24.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1991
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 499
25.1%
0 499
25.1%
3 497
25.0%
1 496
24.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1991
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 499
25.1%
0 499
25.1%
3 497
25.0%
1 496
24.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 499
25.1%
0 499
25.1%
3 497
25.0%
1 496
24.9%

Interactions

2024-05-04T02:38:22.887700image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:37:57.719071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:37:59.827238image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:01.531736image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:03.159105image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:04.841077image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:06.452948image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:08.393293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:10.336951image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:12.732446image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:14.751284image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:16.897742image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:18.842006image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:20.624957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:23.023696image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:37:57.894668image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:37:59.947874image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:01.637539image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:03.268459image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:04.978958image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:06.560782image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:08.515153image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:10.509768image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:12.908237image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:14.982413image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:17.165748image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:18.961951image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:20.741824image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:23.164515image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:37:58.014578image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:00.042120image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:01.743461image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:03.374398image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:05.077487image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:06.732990image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:08.625351image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:10.709886image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:13.045476image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:15.114159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:17.296671image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:19.074792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:20.861938image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:23.305375image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:37:58.129256image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:00.179555image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:01.857413image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:03.483747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:05.171241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:06.880848image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:08.734071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:10.889245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:13.173831image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:15.266037image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:17.422301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:19.225098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:20.999150image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:23.417906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:37:58.227123image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:00.324332image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:01.972134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:03.606087image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:05.274301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:06.991752image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:08.859028image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:11.078497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:13.328306image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:15.374518image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:17.590775image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:19.361189image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:21.141814image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:23.522164image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:37:58.342851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:00.418058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:02.075631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:03.705896image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:05.376124image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:07.128561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:08.975555image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:11.295206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:13.474623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:15.483839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:17.740942image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:19.476871image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:21.317063image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:23.655360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:37:58.453561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:00.543560image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:02.171496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:03.811844image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:05.483527image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:07.294809image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:09.125870image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:11.520396image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:13.608494image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:15.593176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:17.869198image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:19.626871image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:21.493300image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:23.792620image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:37:58.573851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:00.657565image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:02.294434image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:03.922054image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:05.593992image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:07.460711image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:09.234309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:11.689364image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:13.719347image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:15.702975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:17.992025image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:19.769175image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:21.644431image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:24.005743image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:37:58.703440image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:00.781641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:02.426090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:04.056129image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:05.687875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:07.640572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:09.390916image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:11.872326image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:13.875036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:15.829159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:18.120803image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:19.920400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:21.901893image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:24.138496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:37:58.835041image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:00.895128image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:02.562346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:04.177020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:05.807462image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:07.758593image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:09.577095image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:12.040112image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:14.059575image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:15.952658image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:18.247023image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:20.031901image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:22.168050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:24.276164image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:37:59.343525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:01.028452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:02.692484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:04.287991image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:05.916861image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:07.875738image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:09.744455image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:12.171483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:14.197141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:16.092125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:18.355652image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:20.171302image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:22.338846image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:24.393032image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:37:59.484389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:01.194891image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:02.797818image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:04.406573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:06.062366image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:07.992584image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:09.877185image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:12.341259image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:14.338917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:16.235710image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:18.489419image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:20.289712image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:22.463842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:24.542333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:37:59.593333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:01.304285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:02.911464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:04.569942image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:06.213017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:08.110030image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:10.016049image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:12.456820image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:14.485720image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:16.377883image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:18.601792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:20.396571image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:22.587741image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:24.685501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:37:59.719260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:01.408775image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:03.046936image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:04.712906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:06.346733image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:08.218678image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:10.161125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:12.574522image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:14.651903image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:16.617803image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:18.722298image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:20.505386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-04T02:38:22.750246image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-05-04T02:38:32.304924image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
battery_powerblueclock_speeddual_simfcfour_gint_memorym_depmobile_wtn_corespcprice_rangepx_heightpx_widthramsc_hsc_wtalk_timethree_gtouch_screenwifi
battery_power1.0000.0360.0110.0500.0350.000-0.0050.0310.000-0.0300.0290.1270.008-0.011-0.002-0.030-0.0280.0510.0000.0000.000
blue0.0361.0000.0160.0230.0000.0000.0420.002-0.0080.037-0.0100.000-0.005-0.0420.028-0.0050.0040.0160.0180.0000.000
clock_speed0.0110.0161.0000.028-0.0030.0390.008-0.0140.011-0.008-0.0020.000-0.015-0.0120.004-0.032-0.015-0.0130.0350.0350.000
dual_sim0.0500.0230.0281.000-0.0370.000-0.015-0.021-0.007-0.023-0.0160.000-0.0170.0140.042-0.013-0.023-0.0360.0000.0000.007
fc0.0350.000-0.003-0.0371.0000.035-0.0250.0130.028-0.0170.6590.000-0.022-0.0070.023-0.011-0.002-0.0000.0000.0460.046
four_g0.0000.0000.0390.0000.0351.0000.0080.001-0.016-0.031-0.0090.000-0.0200.0110.0070.0270.036-0.0460.5830.0000.000
int_memory-0.0050.0420.008-0.015-0.0250.0081.0000.006-0.035-0.027-0.0330.039-0.000-0.0090.0300.0420.015-0.0030.0280.0230.016
m_dep0.0310.002-0.014-0.0210.0130.0010.0061.0000.022-0.0040.0270.0150.0250.021-0.011-0.025-0.0210.0160.0000.0670.022
mobile_wt0.000-0.0080.011-0.0070.028-0.016-0.0350.0221.000-0.0200.0210.0290.0110.001-0.004-0.036-0.0180.0040.0000.0000.000
n_cores-0.0300.037-0.008-0.023-0.017-0.031-0.027-0.004-0.0201.000-0.0030.004-0.0060.0240.0050.0000.0290.0130.0230.0000.000
pc0.029-0.010-0.002-0.0160.659-0.009-0.0330.0270.021-0.0031.0000.032-0.0150.0050.0310.005-0.0380.0150.0000.0300.000
price_range0.1270.0000.0000.0000.0000.0000.0390.0150.0290.0040.0321.0000.1320.1630.9170.0220.0240.0210.0000.0200.000
px_height0.008-0.005-0.015-0.017-0.022-0.020-0.0000.0250.011-0.006-0.0150.1321.0000.467-0.0300.0520.029-0.0120.0120.0000.061
px_width-0.011-0.042-0.0120.014-0.0070.011-0.0090.0210.0010.0240.0050.1630.4671.0000.0010.0210.0240.0060.0000.0000.031
ram-0.0020.0280.0040.0420.0230.0070.030-0.011-0.0040.0050.0310.917-0.0300.0011.0000.0160.0270.0110.0400.0000.000
sc_h-0.030-0.005-0.032-0.013-0.0110.0270.042-0.025-0.0360.0000.0050.0220.0520.0210.0161.0000.472-0.0170.0170.0140.070
sc_w-0.0280.004-0.015-0.023-0.0020.0360.015-0.021-0.0180.029-0.0380.0240.0290.0240.0270.4721.000-0.0200.0470.0000.000
talk_time0.0510.016-0.013-0.036-0.000-0.046-0.0030.0160.0040.0130.0150.021-0.0120.0060.011-0.017-0.0201.0000.0350.0470.000
three_g0.0000.0180.0350.0000.0000.5830.0280.0000.0000.0230.0000.0000.0120.0000.0400.0170.0470.0351.0000.0000.000
touch_screen0.0000.0000.0350.0000.0460.0000.0230.0670.0000.0000.0300.0200.0000.0000.0000.0140.0000.0470.0001.0000.000
wifi0.0000.0000.0000.0070.0460.0000.0160.0220.0000.0000.0000.0000.0610.0310.0000.0700.0000.0000.0000.0001.000

Missing values

2024-05-04T02:38:25.499784image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T02:38:25.830241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

battery_powerblueclock_speeddual_simfcfour_gint_memorym_depmobile_wtn_corespcpx_heightpx_widthramsc_hsc_wtalk_timethree_gtouch_screenwifiprice_range
084202.201.00.07.00.6188.02.02.020.0756.02549.09.07.0190011
1102110.510.01.053.00.7136.03.06.0905.01988.02631.017.03.071102
256310.512.01.041.00.9145.05.06.01263.01716.02603.011.02.091102
361512.500.00.010.00.8131.06.09.01216.01786.02769.016.08.0111002
4182111.2013.01.044.00.6141.02.014.01208.01212.01411.08.02.0151101
5185900.513.00.022.00.7164.01.07.01004.01654.01067.017.01.0101001
6182101.704.01.010.00.8139.08.010.0381.01018.03220.013.08.0181013
7195400.510.00.024.00.8187.04.00.0512.01149.0700.016.03.051110
8144510.500.00.053.00.7174.07.014.0386.0836.01099.017.01.0201000
950910.612.01.09.00.193.05.015.01137.01224.0513.019.010.0121000
battery_powerblueclock_speeddual_simfcfour_gint_memorym_depmobile_wtn_corespcpx_heightpx_widthramsc_hsc_wtalk_timethree_gtouch_screenwifiprice_range
1981161712.408.01.036.00.885.01.09.0743.01426.0296.05.03.071000
1982188202.0011.01.044.00.8113.08.019.04.0743.03579.019.08.0201103
198367412.911.00.021.00.2198.03.04.0576.01809.01180.06.03.041110
1984146710.500.00.018.00.6122.05.00.0888.01099.03962.015.011.051113
198585802.201.00.050.00.184.01.02.0528.01416.03978.017.016.031103
198679410.510.01.02.00.8106.06.014.01222.01890.0668.013.04.0191100
1987196512.610.00.039.00.2187.04.03.0915.01965.02032.011.010.0161112
1988191100.911.01.036.00.7108.08.03.0868.01632.03057.09.01.051103
1989151200.904.01.046.00.1145.05.05.0336.0670.0869.018.010.0191110
199051012.015.01.045.00.9168.06.016.0483.0754.03919.019.04.021113